Abstract
Satellite remote sensing offers strong opportunities for monitoring agricultural lands, especially in climate-sensitive regions such as Northern Kazakhstan. However, most existing agricultural monitoring platforms rely mainly on optical vegetation indices (e.g., Normalized vegetation index (NDVI) or Enhanced Vegetation Index (EVI)) and provide limited information on soil moisture and surface temperature. As a result, early signs of water and heat stress at the field level are often missed. This study presents a cloud-based agricultural monitoring system that differs from existing solutions by integrating optical, microwave, and thermal satellite data within a unified framework. The system combines Sentinel-2 vegetation indices, SMAP SM, and land surface temperature (LST) from the MODIS MOD11A2 product, processed using Sentinel Hub and Google Earth Engine. All datasets are harmonized into consistent 10-day composites and aggregated at the field scale. The Thermo-Moisture Drought Index (TMDI) was calculated by combining LST and soil moisture (SM), enabling improved characterization of drought conditions compared to vegetation indices alone. Integrating TMDI and EVI highlights stress conditions that are not visible in optical data only, demonstrating a clear advantage over traditional NDVI-based platforms. The developed system provides an operational, cloud-based solution for field-level monitoring, potentially early stress detection, and interactive analysis. Compared to existing systems, it offers improved environmental interpretation by combining multiple satellite sensors, making it a practical decision-support tool for rainfed agriculture in Northern Kazakhstan. This proposed system architecture is adaptable to other agricultural regions with similar characteristics. Furthermore, the framework can be extended with machine learning algorithms to predict crop yields and automatically assess drought risk.